The role of public health surveillance is to collect, analyze and interpret data about biological agents, diseases, and other health events in order to provide timely dissemination of collected information to decision makers. Surveillance activities share several common practices in the way data is collected, managed, transmitted, analyzed, accessed and disseminated. A type of surveillance known as syndromic surveillance can be used for early detection disease outbreaks based on symptoms and human behavior.
It is also known that monitoring diseases and health conditions in companion animal populations can provide early warning signs for emerging diseases, and some studies estimate that as much as 73% of emerging infectious diseases are zoonotic in origin. Companion animals can also provide warnings of possible exposure to pollutants, chemicals, allergens and natural toxins. In particular, such substances originate from the living environment and can have a detrimental effect on animal health as well as human health. If animal health issues manifest earlier than humans, then health care officials may be able to identify possible a toxin pollution event based on animal health monitoring before significant human effects are evident, particularly in light of the privacy concerns with human health data that often results in the obfuscation of time and/or location specifics with symptoms.
However, the raw data relating to human symptoms and/or animal symptoms for public health surveillance can be difficult to assess. Software-based visual analytic tools have been introduced to provide displays of the symptom data in an intuitive way that may be used to identify problem areas, and may include visual analytic tools. The goal of such tools is to provide an intuitive overview of large amounts of data, preferably with the ability to drill down into the data and/or perform additional statistical analysis on select portions of the data.
In the field of health surveillance, a user may view instances of gastrointestinal illness symptoms over a particular area for various times during the year, noting times of year and/or areas when the gastrointestinal illness symptoms occurred in seemingly larger quantities. The user may then perform statistical analysis to determine further information about those specific instances. However, those systems are not capable of both high true positive rates (precision) and low false positive rates (recall).
It has been known in the past to generate visual tools that provide at least some animal health and human health information based on occurrences of detected symptoms. However, many of the display and aberration detection techniques applicable to human symptoms cannot be usefully applied to animal symptom data. Further, as noted above, much of the data collected for human symptoms is often encumbered by privacy concerns, being collected only on the Zip Code level, and is often not being collected on a real-time basis. In addition, the methods used for syndromic surveillance in human data are not necessarily applicable to syndromic surveillance in animal data. Accordingly, there is a need for improved techniques of generating and displaying visual analytics of animal symptoms for public health surveillance purposes.